A turbomachine such as a gas turbine or an internal combustion engine is a system subject to considerable loads. Creep and fatigue affect the machine in extreme conditions due to very high combustion temperatures, pressure ratios, and air flows. As a consequence of their deterioration, the main components of a gas turbine (GT), i.e. the inlet nozzle, the compressor, the combustion chamber, the turbine, the air flow cooler, and the outlet, all contribute—to a different extent—to the degradation of GT performance. The condition of each single component invariably deteriorates with operation time, until it is at least partially restored by some maintenance action.
Turbomachine degradation is a complex process that can be better understood if one clearly distinguishes between the origin and the symptoms of a fault. The initial reason of a specific degradation, in other words the origin of the fault affecting a given component, can be of various nature, such as fouling, corrosion, erosion, etc. Conversely, different faults often create similar observable effects or symptoms, such as degradation of thermodynamic efficiencies and flow capacities. Unfortunately, it is usually impossible or too expensive to measure the origin of the faults directly, and any measurement or monitoring effort is generally restricted to the identification of symptoms.
The origin of a fault affecting a given component of the gas turbine can be of various nature, such as, for example, a contamination of compressor blades, erosion of turbine blades or corrosion of machine parts. Due to its impact on the turbomachine performance, one specific type of fault origin (e.g. compressor fouling) calls for one specific maintenance action (e.g. compressor washing), while a different fault origin calls for a different maintenance action. Accordingly, there is a need to analyze the measured degradation symptoms continuously, in order to infer their root cause(s), i.e. to localize the origin of the fault(s) in progress. The result of this diagnosis may then be used to optimize the operation and maintenance strategy. Such an optimized maintenance schedule ensures economic and safe operation, and assists in predicting the remaining life of the major components. At this point, the main problem consists in going from the (observed) symptoms back to the origin of the faults. This is a kind of “inverse problem”, as in reality, faults in diverse locations of the machine cause symptoms to appear, wherein as mentioned above, different faults often create similar observable effects or degradation symptoms.
In the patent application EP-A 1 418 481 a framework for aero gas turbine diagnosis is proposed which distinguishes between rapid deterioration due to singular system fault events and gradual deterioration due to damage accumulation of all engine components. A measurement Δ vector, comprising deviations from a reference of some gas path parameter data such as rotor speed, temperatures and pressures, reflects the effects of a multitude of possible engine/sensor system fault occurrences as well as random measurement noise. From this measurement Δ vector at a discrete time k, a total fault vector xk comprising the engine system and sensor faults as the current states is estimated within a Module Performance Analysis. Apart from a reference to statistical tests or neural networks, the estimation method is not detailed. If a rapid deterioration event is in progress, single fault or root cause isolation is performed, based on the change ΔΔk in the measurement Δ vector w. r. t. the previous measurement at time k−1. Otherwise, multiple fault isolation is performed to yield an updated error vector, based on the cumulative share ZkMFI of the measurement Δ vector assigned to gradual deterioration. Because the “fault” vector xk actually corresponds to symptoms (like reduction of fan efficiency etc.), this method allows to reconstruct and distinguish between the different symptoms; however it does not yield the faults' origin.
The goal of gas turbine performance diagnosis is to accurately detect, isolate and assess performance changes, system malfunctions and instrumentation problems. Among a number of other techniques, Gas Path Analysis (GPA) is a well established framework for estimating shifts in performance from the knowledge of measured parameters, such as power, engine speeds, temperatures, pressures or fuel flow, taken along the gas path of the turbine. Discernable shifts in these measured parameters provide the requisite information for determining the underlying shift in engine operation from a presumed reference, nominal or initial state, i.e. the degradation symptoms. GPA allows engine performance deterioration to be identified in terms of a degradation of independent parameters or system states such as thermodynamic efficiencies, flow capacities and inlet/outlet filter areas. The unpublished European Patent Application 05405270.9 discloses a method of monitoring the evolution of different degradation symptoms or health parameters representing a slowly degrading real or simulated system. This application is incorporated herein for all purposes by way of reference. All GPA-based methods essentially end up at this point, providing some estimates of the performance deteriorations or degradations i.e., of the symptoms. However, the problem of identifying and localizing the root cause of the symptoms, e.g. the answer to the question: “is the efficiency deterioration caused by turbine erosion or by fouling?”, is not provided by these methods and left to a subsequent analysis or diagnosis step.
On the other hand, the patent application EP 1 103 926 relates to model-based diagnostics for aeronautical gas turbine engines. Sensor values (speed, temperatures, pressures) and virtual or model parameters (stall margins, specific fuel consumption, airflows, fan/compressor/turbine efficiencies) are evaluated in a fault detection & isolation classifier (a feed-forward neural network or a linear regressor), to identify specific fault classes and output a diagnosis. The neural network and the linear regressor are trained with sets of engine or model data, including both simulated unfaulted engines and simulated engines with the specific faults to be classified in the diagnosis.